Biology subjects

Wesolowska-Andersen, A.

Publications and source records attributed to Wesolowska-Andersen, A..

2 recordsLinked to original sources

bioRxiv2020

LRP5 promotes adipose progenitor cell fitness and adipocyte insulin sensitivity

WNT signalling is a developmental pathway which plays an important role in post-natal bone accrual. We have previously shown, that in addition to exhibiting extreme high bone mass, subjects with rare gain-of-function (GoF) mutations in the WNT co-receptor LRP5 also display increased lower-body fat mass. Here, we demonstrate using human physiological studies in GoF LRP5 mutation carriers and glucose uptake assays in LRP5 knockdown (KD) adipocytes that LRP5 promotes adipocyte insulin sensitivity. We also show that a low frequency missense variant in LRP5 shown to be associated with low heel bone mineral density in a genome wide association study meta-analysis, is associated with reduced leg fat mass. Finally, using genome wide transcriptomic analyses and in vitro functional studies in LRP5-KD adipose progenitors (APs) we demonstrate that LRP5 plays an essential role in maintaining AP fitness i.e. functional characteristics. Pharmacological activation of LRP5 signalling in adipose tissue provides a promising strategy to prevent the redistribution of adipose tissue and metabolic sequela associated with obesity and ageing.

cell biology↗

bioRxiv2019

Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals

Genome-wide association analyses have uncovered multiple genomic regions associated with T2D, but identification of the causal variants at these remains a challenge. There is growing interest in the potential of deep learning models - which predict epigenome features from DNA sequence - to support inference concerning the regulatory effects of disease-associated variants. Here, we evaluate the advantages of training convolutional neural network (CNN) models on a broad set of epigenomic features collected in a single disease-relevant tissue - pancreatic islets in the case of type 2 diabetes (T2D) - as opposed to models trained on multiple human tissues. We report convergence of CNN-based metrics of regulatory function with conventional approaches to variant prioritization - genetic fine-mapping and regulatory annotation enrichment. We demonstrate that CNN-based analyses can refine association signals at T2D-associated loci and provide experimental validation for one such signal. We anticipate that these approaches will become routine in downstream analyses of GWAS.

bioinformatics↗